from typing import Any

import torch
from torch import nn
import math


# extract from d2l

class AttentionBase(nn.Module):
    def _forward_unimplemented(self, *input: Any) -> None:
        pass

    def __init__(self, **kwargs):
        super(AttentionBase, self).__init__(**kwargs)

    # Shape of `queries`: (`batch_size`, no. of queries, `d`)
    # Shape of `keys`: (`batch_size`, no. of key-value pairs, `d`)
    # Shape of `values`: (`batch_size`, no. of key-value pairs, value
    # dimension)
    # Shape of `valid_lens`: (`batch_size`,) or (`batch_size`, no. of queries)
    def forward(self, queries, keys, values, valid_lens=None):
        raise NotImplementedError


class DotProductAttention(AttentionBase):
    """Scaled dot product attention."""
    def __init__(self, dropout, **kwargs):
        super(DotProductAttention, self).__init__(**kwargs)
        self.dropout = nn.Dropout(dropout)

    # Shape of `queries`: (`batch_size`, no. of queries, `d`)
    # Shape of `keys`: (`batch_size`, no. of key-value pairs, `d`)
    # Shape of `values`: (`batch_size`, no. of key-value pairs, value
    # dimension)
    # Shape of `valid_lens`: (`batch_size`,) or (`batch_size`, no. of queries)
    def forward(self, queries, keys, values, valid_lens=None):
        d = queries.shape[-1]
        # Set `transpose_b=True` to swap the last two dimensions of `keys`
        scores = torch.bmm(queries, keys.transpose(1, 2)) / math.sqrt(d)
        self.attention_weights = masked_softmax(scores, valid_lens)
        return torch.bmm(self.dropout(self.attention_weights), values)


class AdditiveAttention(AttentionBase):
    def __init__(self, key_size, query_size, num_hiddens, dropout, **kwargs):
        super(AdditiveAttention, self).__init__(**kwargs)
        self.W_k = nn.Linear(key_size, num_hiddens, bias=False)
        self.W_q = nn.Linear(query_size, num_hiddens, bias=False)
        self.w_v = nn.Linear(num_hiddens, 1, bias=False)
        self.dropout = nn.Dropout(dropout)

    def forward(self, queries, keys, values, valid_lens=None):
        queries, keys = self.W_q(queries), self.W_k(keys)
        # After dimension expansion, shape of `queries`: (`batch_size`, no. of
        # queries, 1, `num_hiddens`) and shape of `keys`: (`batch_size`, 1,
        # no. of key-value pairs, `num_hiddens`). Sum them up with
        # broadcasting
        features = queries.unsqueeze(2) + keys.unsqueeze(1)
        features = torch.tanh(features)
        # There is only one output of `self.w_v`, so we remove the last
        # one-dimensional entry from the shape. Shape of `scores`:
        # (`batch_size`, no. of queries, no. of key-value pairs)
        scores = self.w_v(features).squeeze(-1)
        self.attention_weights = masked_softmax(scores, valid_lens)
        # Shape of `values`: (`batch_size`, no. of key-value pairs, value
        # dimension)
        return torch.bmm(self.dropout(self.attention_weights), values)


def masked_softmax(X, valid_lens):
    """Perform softmax operation by masking elements on the last axis."""
    # `X`: 3D tensor, `valid_lens`: 1D or 2D tensor
    if valid_lens is None:
        return nn.functional.softmax(X, dim=-1)
    else:
        shape = X.shape
        if valid_lens.dim() == 1:
            valid_lens = torch.repeat_interleave(valid_lens, shape[1])
        else:
            valid_lens = valid_lens.reshape(-1)
        # On the last axis, replace masked elements with a very large negative
        # value, whose exponentiation outputs 0
        X = sequence_mask(X.reshape(-1, shape[-1]), valid_lens,
                          value=float('-inf'))
        return nn.functional.softmax(X.reshape(shape), dim=-1)


def sequence_mask(X, valid_len, value=-1e7):
    """Mask irrelevant entries in sequences."""
    maxlen = X.size(1)
    # mask = torch.arange(maxlen, dtype=torch.float32,
    #                     device=X.device)[None, :] < valid_len[:, None]
    # X[~mask] = value
    mask = torch.arange(maxlen, dtype=torch.float32,
                        device=X.device)[None, :] >= valid_len[:, None]
    X.masked_fill_(mask, value)
    return X
